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<div class="section xpack">
<div class="titlepage"><div><div>
<h2 class="title">
<a id="ml-settings"></a>Machine learning settings in Elasticsearch<a class="edit_me edit_me_private" rel="nofollow" title="Editing on GitHub is available to Elastic" href="https://github.com/elastic/elasticsearch/edit/7.7/docs/reference/settings/ml-settings.asciidoc">edit</a><a class="xpack_tag" href="/subscriptions"></a>
</h2>
</div></div></div>

<p>You do not need to configure any settings to use machine learning. It is enabled by default.</p>
<div class="important admon">
<div class="icon"></div>
<div class="admon_content">
<p>Machine learning uses SSE4.2 instructions, so will only work on machines whose
CPUs <a href="https://en.wikipedia.org/wiki/SSE4#Supporting_CPUs" class="ulink" target="_top">support</a> SSE4.2. If you
run Elasticsearch on older hardware you must disable machine learning (by setting <code class="literal">xpack.ml.enabled</code>
to <code class="literal">false</code>).</p>
</div>
</div>
<p>All of these settings can be added to the <code class="literal">elasticsearch.yml</code> configuration file.
The dynamic settings can also be updated across a cluster with the
<a class="xref" href="cluster-update-settings.html" title="Cluster update settings API">cluster update settings API</a>.</p>
<div class="tip admon">
<div class="icon"></div>
<div class="admon_content">
<p>Dynamic settings take precedence over settings in the <code class="literal">elasticsearch.yml</code>
file.</p>
</div>
</div>
<h4>
<a id="general-ml-settings"></a>General machine learning settings<a class="edit_me edit_me_private" rel="nofollow" title="Editing on GitHub is available to Elastic" href="https://github.com/elastic/elasticsearch/edit/7.7/docs/reference/settings/ml-settings.asciidoc">edit</a>
</h4>
<div class="variablelist">
<dl class="variablelist">
<dt>
<span class="term">
<code class="literal">node.ml</code>
</span>
</dt>
<dd>
<p>
Set to <code class="literal">true</code> (default) to identify the node as a <em>machine learning node</em>.<br>
</p>
<p>If set to <code class="literal">false</code> in <code class="literal">elasticsearch.yml</code>, the node cannot run jobs. If set to
<code class="literal">true</code> but <code class="literal">xpack.ml.enabled</code> is set to <code class="literal">false</code>, the <code class="literal">node.ml</code> setting is
ignored and the node cannot run jobs. If you want to run jobs, there must be at
least one machine learning node in your cluster.<br></p>
<div class="important admon">
<div class="icon"></div>
<div class="admon_content">
<p>On dedicated coordinating nodes or dedicated master nodes, disable
the <code class="literal">node.ml</code> role.</p>
</div>
</div>
</dd>
<dt>
<span class="term">
<code class="literal">xpack.ml.enabled</code>
</span>
</dt>
<dd>
<p>
Set to <code class="literal">true</code> (default) to enable machine learning on the node.
</p>
<p>If set to <code class="literal">false</code>, the machine learning APIs are disabled on the node. Therefore the node
cannot open jobs, start datafeeds, or receive transport (internal) communication
requests related to machine learning APIs. If the node is a coordinating node, machine learning requests
from clients (including Kibana) also fail. For more information about disabling
machine learning in specific Kibana instances, see
<a href="/guide/en/kibana/7.7/ml-settings-kb.html" class="ulink" target="_top">Kibana machine learning settings</a>.</p>
<div class="important admon">
<div class="icon"></div>
<div class="admon_content">
<p>If you want to use machine learning features in your cluster, it is recommended
that you set <code class="literal">xpack.ml.enabled</code> to <code class="literal">true</code> on all nodes. This is the
default behavior. At a minimum, it must be enabled on all master-eligible nodes.
If you want to use machine learning features in clients or Kibana, it must also be enabled on
all coordinating nodes.</p>
</div>
</div>
</dd>
<dt>
<span class="term">
<code class="literal">xpack.ml.inference_model.cache_size</code>
</span>
</dt>
<dd>
The maximum inference cache size allowed. The inference cache exists in the JVM
heap on each ingest node. The cache affords faster processing times for the
<code class="literal">inference</code> processor. The value can be a static byte sized value (i.e. "2gb")
or a percentage of total allocated heap. The default is "40%".
</dd>
<dt>
<span class="term">
<code class="literal">xpack.ml.inference_model.time_to_live</code>
</span>
</dt>
<dd>
The time to live (TTL) for models in the inference model cache. The TTL is
calculated from last access. The <code class="literal">inference</code> processor attempts to load the
model from cache. If the <code class="literal">inference</code> processor does not receive any documents
for the duration of the TTL, the referenced model is flagged for eviction from
the cache. If a document is processed later, the model is again loaded into the
cache. Defaults to <code class="literal">5m</code>.
</dd>
<dt>
<span class="term">
<code class="literal">xpack.ml.max_inference_processors</code> (<a class="xref" href="cluster-update-settings.html" title="Cluster update settings API">Dynamic</a>)
</span>
</dt>
<dd>
The total number of <code class="literal">inference</code> type processors allowed across all ingest
pipelines. Once the limit is reached, adding an <code class="literal">inference</code> processor to
a pipeline is disallowed. Defaults to <code class="literal">50</code>.
</dd>
<dt>
<span class="term">
<code class="literal">xpack.ml.max_machine_memory_percent</code> (<a class="xref" href="cluster-update-settings.html" title="Cluster update settings API">Dynamic</a>)
</span>
</dt>
<dd>
The maximum percentage of the machine’s memory that machine learning may use for running
analytics processes. (These processes are separate to the Elasticsearch JVM.) Defaults to
<code class="literal">30</code> percent. The limit is based on the total memory of the machine, not current
free memory. Jobs will not be allocated to a node if doing so would cause the
estimated memory use of machine learning jobs to exceed the limit.
</dd>
<dt>
<span class="term">
<code class="literal">xpack.ml.max_model_memory_limit</code> (<a class="xref" href="cluster-update-settings.html" title="Cluster update settings API">Dynamic</a>)
</span>
</dt>
<dd>
The maximum <code class="literal">model_memory_limit</code> property value that can be set for any job on
this node. If you try to create a job with a <code class="literal">model_memory_limit</code> property value
that is greater than this setting value, an error occurs. Existing jobs are not
affected when you update this setting. For more information about the
<code class="literal">model_memory_limit</code> property, see <a class="xref" href="ml-put-job.html#put-analysislimits">`analysis_limits`</a>.
</dd>
</dl>
</div>
<div class="variablelist">
<a id="xpack.ml.max_open_jobs"></a>
<dl class="variablelist">
<dt>
<span class="term">
<code class="literal">xpack.ml.max_open_jobs</code> (<a class="xref" href="cluster-update-settings.html" title="Cluster update settings API">Dynamic</a>)
</span>
</dt>
<dd>
The maximum number of jobs that can run simultaneously on a node. Defaults to
<code class="literal">20</code>. In this context, jobs include both anomaly detection jobs and data frame analytics jobs.
The maximum number of jobs is also constrained by memory usage. Thus if the
estimated memory usage of the jobs would be higher than allowed, fewer jobs will
run on a node. Prior to version 7.1, this setting was a per-node non-dynamic
setting. It became a cluster-wide dynamic setting in version 7.1. As a result,
changes to its value after node startup are used only after every node in the
cluster is running version 7.1 or higher. The maximum permitted value is <code class="literal">512</code>.
</dd>
<dt>
<span class="term">
<code class="literal">xpack.ml.node_concurrent_job_allocations</code> (<a class="xref" href="cluster-update-settings.html" title="Cluster update settings API">Dynamic</a>)
</span>
</dt>
<dd>
The maximum number of jobs that can concurrently be in the <code class="literal">opening</code> state on
each node. Typically, jobs spend a small amount of time in this state before
they move to <code class="literal">open</code> state. Jobs that must restore large models when they are
opening spend more time in the <code class="literal">opening</code> state. Defaults to <code class="literal">2</code>.
</dd>
</dl>
</div>
<h4>
<a id="advanced-ml-settings"></a>Advanced machine learning settings<a class="edit_me edit_me_private" rel="nofollow" title="Editing on GitHub is available to Elastic" href="https://github.com/elastic/elasticsearch/edit/7.7/docs/reference/settings/ml-settings.asciidoc">edit</a>
</h4>
<p>These settings are for advanced use cases; the default values are generally
sufficient:</p>
<div class="variablelist">
<dl class="variablelist">
<dt>
<span class="term">
<code class="literal">xpack.ml.enable_config_migration</code> (<a class="xref" href="cluster-update-settings.html" title="Cluster update settings API">Dynamic</a>)
</span>
</dt>
<dd>
Reserved.
</dd>
<dt>
<span class="term">
<code class="literal">xpack.ml.max_anomaly_records</code> (<a class="xref" href="cluster-update-settings.html" title="Cluster update settings API">Dynamic</a>)
</span>
</dt>
<dd>
The maximum number of records that are output per bucket. The default value is
<code class="literal">500</code>.
</dd>
<dt>
<span class="term">
<code class="literal">xpack.ml.max_lazy_ml_nodes</code> (<a class="xref" href="cluster-update-settings.html" title="Cluster update settings API">Dynamic</a>)
</span>
</dt>
<dd>
<p>
The number of lazily spun up Machine Learning nodes. Useful in situations
where ML nodes are not desired until the first Machine Learning Job
is opened. It defaults to <code class="literal">0</code> and has a maximum acceptable value of <code class="literal">3</code>.
If the current number of ML nodes is <code class="literal">&gt;=</code> than this setting, then it is
assumed that there are no more lazy nodes available as the desired number
of nodes have already been provisioned. When a job is opened with this
setting set at <code class="literal">&gt;0</code> and there are no nodes that can accept the job, then
the job will stay in the <code class="literal">OPENING</code> state until a new ML node is added to the
cluster and the job is assigned to run on that node.
</p>
<div class="important admon">
<div class="icon"></div>
<div class="admon_content">
<p>This setting assumes some external process is capable of adding ML nodes
to the cluster. This setting is only useful when used in conjunction with
such an external process.</p>
</div>
</div>
</dd>
<dt>
<span class="term">
<code class="literal">xpack.ml.process_connect_timeout</code> (<a class="xref" href="cluster-update-settings.html" title="Cluster update settings API">Dynamic</a>)
</span>
</dt>
<dd>
The connection timeout for machine learning processes that run separately from the Elasticsearch JVM.
Defaults to <code class="literal">10s</code>. Some machine learning processing is done by processes that run separately
to the Elasticsearch JVM. When such processes are started they must connect to the Elasticsearch
JVM. If such a process does not connect within the time period specified by this
setting then the process is assumed to have failed. Defaults to <code class="literal">10s</code>. The minimum
value for this setting is <code class="literal">5s</code>.
</dd>
</dl>
</div>
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